In the present state of the art, the transformation of the Internet into an important and ubiquitous commercial infrastructure has not only created rapidly rising bandwidth demand but also significantly changed consumer expectations in terms of performance, security, and services. Consequently, service providers need to not only evolve their networks to higher and higher speeds, but also need to plan for the introduction of new services and mechanisms so as to address the varied requirements of different customers. At the same time service providers would like to maximize the sharing of the costly backbone infrastructure in a manner that enables them to control usage of network resources in accordance with service pricing and revenue potential. The two trends of rapidly rising bandwidth demand and rising need for differentiation have resulted in efforts to define mechanisms for service differentiation.
Internet applications and users have very diverse quality of service expectations, making the “best-effort” service (or the same-service-to-all) model of the current Internet inadequate and limiting. The prevalent Internet “best-effort” service model does not permit users to obtain better service, no matter how critical their requirements are, and no matter how much they are willing to pay for better service. Clearly, with the increased use of the Internet for commercial purposes, this model of no differentiation is not satisfactory since the service providers do not have the means to meet an existing market demand. There is a widespread consensus today that the Internet architecture has to be extended with service differentiation mechanisms so that certain users and applications can get better service than others at a higher cost.
“Best efforts” may be sufficient in some instances with appropriate provisioning. Nevertheless, some form of differentiation is still desirable. When there is a focused overload to part of a network (such as when a popular web site is heavily accessed, or an unexpected event occurs) the network devices (e.g., switches, routers) have no mechanisms to treat different customers differently. When such events occur, insufficient resources are available for providing reasonable service to all users. Over-provisioning network bandwidth and keeping the network lightly loaded in order to support differentiated services for select customers is not a cost-effective solution, and cannot be achieved at all times.
As a result, from the network device perspective, tools have been developed for providing differentiated services based on the several operations. Packet classification tools can distinguish packets and group them according to their different requirements. Buffer management techniques determine how much buffer space should be given to certain kinds of network traffic and which packets should be discarded in case of congestion. Packet scheduling techniques decide the packet service order so as to meet the bandwidth and delay requirements of different types of traffic.
Some mechanisms for delay and loss rate differentiation have recently been proposed in:    [1]. C. Dovrolis and P. Ramanathan, “A Case for Relative Differentiated Services and the Proportional Differentiated Model,” IEEE Network, Sept./October 1999, pp. 26–341    [2]. C. Dovrolis, D. Stilliadis, and P. Ramanathan, “Proportional Differentiated Service: Delay Differentiation and Packet Scheduling,” Proc. ACM SIGCOMM'99, 1999, pp. 109–120;    [3]. C. Dovrolis and D. Stilliadis, “Relative Differentiated Service in the Internet: Issues and Mechanisms,” Proc. ACM SIGMETRICS'99, 1999; and    [4]. C. Dovrolis and P. Ramanathan, “Proportional Differentiated Services, Part II: Loss rate Differentiation and Packet Dropping,” Proc. 2000 Int. Workshop on Quality of Service (IWQoS), Pittsburg Pa., June 2000.
A deficiency of the above-mentioned systems is that they fail to take active queue management into consideration which is needed to effectively manage queues in TCP/IP networks. Active queue management schemes include RED (random early detection) as proposed in:    [5]. S. Floyd and V. Jacobson, “Random Early Detection Gateways for Congestion Avoidance,” IEEE/ACM Trans. Networking, Vol. 1, No. 4, August 1993, pp. 397–413; and    [6]. B. Braden, D. Clark, J. Crowcroft, B. Davie, D. Estrin, S. Floyd, V. Jacobson, G. Minshall, C. Partridge, L. Peterson, K. K. Ramakrishnan, S. Shenker, J. Wroclawski, L. Zhang, “Recommendation on Queue Management and Congestion Avoidance in the Internet,” IETF RFC 2309, April 1998.
The RED technique aims to detect incipient congestion early and to convey congestion notification to the end-systems, allowing them to reduce their transmission rates before queues in the network overflow and packets are dropped. The basic RED scheme (and its newer variants) maintains an average of the queue length which it uses together with a number of queue thresholds to detect congestion. RED schemes drop incoming packets in a random probabilistic manner where the probability is a function of recent buffer fill history. The objective is to provide a more equitable distribution of packet loss, avoid the synchronization of flows and at the same time improve the utilization of the network.
In a relative differentiated service model, all traffic is grouped into N classes of service. For each class i, the service provided to class i will be better (or at least no worse) than the service provided to class (i−1), where 1<i≦N, in terms of local (per-hop) performance measures of queuing delays and packet losses. This model is discussed in references [1], [2], and [3] identified above. The “or at least no worse” clause is included for levels of low network activity where all classes experience the same quality of service. In this model, an application can select its Class Per-Hop Behavior (CPHB), as defined by the Internet Engineering Task Force (IETF), to select the appropriate level of service. However, this level of service is relative to the other classes in the network and is not an absolute guarantee (such as an end-to-end delay bound or bandwidth), since there is no admission control and resource reservation. The relative differentiated services model assures that the performance of the selected class will be relatively better than the performance of lower classes in the network.
Two principles have been proposed in order for the relative differentiated service model to be effective for both users and network operators. First, a model must be predictable, such that the differentiation is consistent (a higher class is better or at least no worse than a lower class) and the differentiation is independent of class loads. Second, the model must be controllable, such that the network operators can select the appropriate level of spacing between the classes based on their pricing or policy criteria. From these two principles, the proportional differentiation model was proposed. The premise of the proportional differentiation model is that the performance measures for packet forwarding at each hop are ratioed proportionally via the use of class differentiation parameters. Two schemes, Backlog Proportional Rate (BPR) and Waiting-Time Priority (WTP) scheduling were proposed for applying the proportional differentiation model to packet delay. The BPR and WTP schedulers can be applied to delay-sensitive applications such as IP-telephony and video-conferencing.
The aforementioned references, proposed a proportional loss dropper that uses the notion of a loss history buffer (LHB) which captures the loss information for the last K packets received. This loss dropper is invoked during buffer overflow and selects a class to drop packets based on the distance of the class from the normalized loss rates of the other classes. This loss dropper can be used in conjunction with the BPR and WTP schedulers to achieve differentiation according to both loss as well as delay.
The proportional differentiation model disclosed in the aforementioned references, provides a way to control the quality spacing between classes locally at each hop, independent of the class loads. According to this model, certain forwarding performance metrics are ratioed proportionally to the class differentiation parameters that the network operator chooses, leading to controllable service differentiation. Resource management, specifically, buffer management in network devices involve both dimensions of time (packet scheduling) and buffer space (packet discarding). Consequently, queuing delay and packet loss are two performance measures that can be used for proportional service differentiation.
Where {overscore (l)}i is the average loss rate for class i, the proportional differentiation model, in the case of packet drops, requires that the class loss rates are spaced as                                                                         l                _                            i                                                      l                _                            j                                =                                    σ              i                                      σ              j                                      ,                                  ⁢                  1          ≤          i                ,                  j          ≤          N                                    (        1        )            
The parameters σi, are the loss rate differentiation parameters and they are ordered as σ1>σ2> . . . >σN>0. In this model, higher classes have better performance in terms of loss rates. The loss rate differentiation parameters σ1>σ2> . . . >σN>0 provide the network operator with tuning knobs for adjusting the quality spacing between classes, independent of the class loads. When this spacing is feasible in short time scales, it can lead to predictable and controllable class differentiation, which are two important features for any relative differentiation model.
The present state of the art fails to propose a loss rate differentiation mechanism designed around an active queue management scheme to determine both when a packet should be dropped and from what class the packet should be dropped.
In view of the foregoing, it would be desirable to provide a technique for strategic distribution of losses within a network which overcomes the above-described inadequacies and shortcomings. More particularly, it would be desirable to provide a technique for distributing losses based on service class when such losses are unavoidable in a network in an efficient and cost effective manner.